THE SOLUTION TO THE PROJECT SCHEDULING PROBLEM BY USING AN IMPROVED GENETIC ALGORITHM
DOI:
https://doi.org/10.18173/2354-1059.2024-0035Keywords:
project scheduling, scheduling problem, genetic algorithmAbstract
Nowadays, managing and allocating resources for projects has become increasingly essential for managers. A critical factor affecting the success of a project is the work assignment plan for workers to optimize the completion time. Current solutions to project scheduling problems have not been thoroughly addressed; thus, in this study, we model the labor assignment process in project production as a scheduling problem. To solve this problem, we use an improved genetic algorithm named GA-RT (Genetic Algorithm with Random Crossover and Negative Tournament Selection) and conduct experiments on the iMOPSE standard dataset. Experimental results show that the proposed GA-RT algorithm can effectively solve the project scheduling problem, achieving better performance compared to existing algorithms.
References
[1] Dang QH, Nguyen TL, Nguyen DC, Xiong N, (2020). Effective Evolutionary Algorithm for Solving the Real-Resource Constrained Scheduling Problem. Journal of Advanced Transportation, 2020(1). DOI: 10.1155/2020/8897710.
[2] Błazewicz J, Lenstra JK & Rinnooy Kan AHG, (1983). Scheduling subject to resource constraints: classification and complexity. Discrete Applied Mathematics, 5(1), 11-24.
[3] Skowroński M, Myszkowski PB, Kwiatek P & Adamski M, (2013). Tabu Search approach for Multi–Skill Resource–Constrained Project Scheduling Problem. 2013 Federated Conference on Computer Science and Information Systems, Krakow, Poland, 153-158.
[4] Hosseinian AH & Baradaran V, (2019). An Evolutionary Algorithm Based on a Hybrid Multi-Attribute Decision Making Method for the Multi-Mode Multi-Skilled Resource-Constrained Project Scheduling Problem. Journal of Optimization in Industrial Engineering, 12(2), 155-178.
[5] Hosseinian AH & Baradaran V, (2020), P-GWO and MOFA: two new algorithms for the MSRCPSP with the deterioration effect and financial constraints (a case study of a gas treating company). Applied Intelligence, 50, 2151-2176.
[6] Christian A, Demassey S & Néron E, (2008). Resource Constrained Project Scheduling: Models, Algorithms, Extensions and Applications (1st ed), Wiley-ISTE.
[7] Klein R (2012). Scheduling of Resource-Constrained Projects, Springer Science & Business Media (2000th ed), Kluwer Academic Publishers.
[8] Myszkowski PB, Laszczyk M, Nikulin I & Skowro M, (2019). iMOPSE: a library for bicriteria optimization in Multi-Skill Resource-Constrained Project Scheduling Problem. Soft Computing Journal, 23(11), 2297-3410.
[9] Myszkowski PB, Skowronski ME & Sikora K, (2015). A new benchmark dataset for Multi-Skill Resource-Constrained Project Scheduling Problem, 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), Lodz, Poland, 129-138. DOI: 10.15439/2015F273.
[10] Katoch S, Chauhan1 SS & Kumar V, (2021). A review on genetic algorithm: past, present, and future. Multimedia Tools and Applications, 80, 8091-8126.
[11] Kolisch R & Sprecher A, (1997). PSPLIB-a project scheduling problem library: OR software-ORSEP operations research software exchange program. European Journal of Operational Research, 96(1), 205-216.
[12] Hosseinian AH, Baradaran V & Bashiri M, (2019). Modeling of the time-dependent multi-skilled RCPSP considering learning effect. Journal of Modelling in Management, 10(2), 521-558.
[13] Hosseinian AH & Baradaran V, (2019). Detecting communities of workforces for the multi-skill resource-constrained project scheduling problem: A dandelion solution approach. Journal of Industrial and Systems Engineering, 12(Special issue on Project Management and Control), 72-99.
